






import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a_		:    Tuple   							=     logging.get_logger(__name__)

a_		:    Dict   							=     {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}

class 		_snake_case	(	A__       ):
			_lowercase		:     List[str]        =				'''conditional_detr'''
			_lowercase		:     Optional[Any]        =				['''past_key_values''']
			_lowercase		:     Optional[Any]        =				{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}






			def __init__(							self		,       a=True		,       a=None		,       a=3		,       a=300		,       a=6		,       a=2048		,       a=8		,       a=6		,       a=2048		,       a=8		,       a=0.0		,       a=0.0		,       a=True		,       a="relu"		,       a=256		,       a=0.1		,       a=0.0		,       a=0.0		,       a=0.02		,       a=1.0		,       a=False		,       a="sine"		,       a="resnet50"		,       a=True		,       a=False		,       a=2		,       a=5		,       a=2		,       a=1		,       a=1		,       a=2		,       a=5		,       a=2		,       a=0.25		,       **a		,       )   ->							List[str]:
						if backbone_config is not None and use_timm_backbone:
									raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.')

						if not use_timm_backbone:
									if backbone_config is None:
												logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
												SCREAMING_SNAKE_CASE							=			CONFIG_MAPPING['resnet'](out_features=['stage4'])
									elif isinstance(a		,       a):
												SCREAMING_SNAKE_CASE							=			backbone_config.get('model_type')
												SCREAMING_SNAKE_CASE							=			CONFIG_MAPPING[backbone_model_type]
												SCREAMING_SNAKE_CASE							=			config_class.from_dict(a)

						SCREAMING_SNAKE_CASE							=			use_timm_backbone
						SCREAMING_SNAKE_CASE							=			backbone_config
						SCREAMING_SNAKE_CASE							=			num_channels
						SCREAMING_SNAKE_CASE							=			num_queries
						SCREAMING_SNAKE_CASE							=			d_model
						SCREAMING_SNAKE_CASE							=			encoder_ffn_dim
						SCREAMING_SNAKE_CASE							=			encoder_layers
						SCREAMING_SNAKE_CASE							=			encoder_attention_heads
						SCREAMING_SNAKE_CASE							=			decoder_ffn_dim
						SCREAMING_SNAKE_CASE							=			decoder_layers
						SCREAMING_SNAKE_CASE							=			decoder_attention_heads
						SCREAMING_SNAKE_CASE							=			dropout
						SCREAMING_SNAKE_CASE							=			attention_dropout
						SCREAMING_SNAKE_CASE							=			activation_dropout
						SCREAMING_SNAKE_CASE							=			activation_function
						SCREAMING_SNAKE_CASE							=			init_std
						SCREAMING_SNAKE_CASE							=			init_xavier_std
						SCREAMING_SNAKE_CASE							=			encoder_layerdrop
						SCREAMING_SNAKE_CASE							=			decoder_layerdrop
						SCREAMING_SNAKE_CASE							=			encoder_layers
						SCREAMING_SNAKE_CASE							=			auxiliary_loss
						SCREAMING_SNAKE_CASE							=			position_embedding_type
						SCREAMING_SNAKE_CASE							=			backbone
						SCREAMING_SNAKE_CASE							=			use_pretrained_backbone
						SCREAMING_SNAKE_CASE							=			dilation
						# Hungarian matcher
						SCREAMING_SNAKE_CASE							=			class_cost
						SCREAMING_SNAKE_CASE							=			bbox_cost
						SCREAMING_SNAKE_CASE							=			giou_cost
						# Loss coefficients
						SCREAMING_SNAKE_CASE							=			mask_loss_coefficient
						SCREAMING_SNAKE_CASE							=			dice_loss_coefficient
						SCREAMING_SNAKE_CASE							=			cls_loss_coefficient
						SCREAMING_SNAKE_CASE							=			bbox_loss_coefficient
						SCREAMING_SNAKE_CASE							=			giou_loss_coefficient
						SCREAMING_SNAKE_CASE							=			focal_alpha
						super().__init__(is_encoder_decoder=a		,       **a)






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						return self.encoder_attention_heads






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						return self.d_model







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			copy.deepcopy(self.__dict__)
						if self.backbone_config is not None:
									SCREAMING_SNAKE_CASE							=			self.backbone_config.to_dict()
						SCREAMING_SNAKE_CASE							=			self.__class__.model_type
						return output



class 		_snake_case	(	A__       ):
			_lowercase		:     int        =				version.parse('''1.11'''       )






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Mapping[str, Mapping[int, str]]:
						return OrderedDict(
						    [
						        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
						        ('pixel_mask', {0: 'batch'}),
						    ])






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							float:
						return 1E-5







			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						return 12












"""simple docstring"""


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass




@dataclass
class 						__UpperCamelCase     :
					lowercase_    :				int
					lowercase_    :				TreeNode | None      =  None
					lowercase_    :				TreeNode | None      =  None


__SCREAMING_SNAKE_CASE   =   namedtuple('CoinsDistribResult', 'moves excess')


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if root is None:
												return 0

						# Validation
						def count_nodes(a__ ) -> int:
												if node is None:
																		return 0

												return count_nodes(node.left ) + count_nodes(node.right ) + 1

						def count_coins(a__ ) -> int:
												if node is None:
																		return 0

												return count_coins(node.left ) + count_coins(node.right ) + node.data

						if count_nodes(a__ ) != count_coins(a__ ):
												raise ValueError('The nodes number should be same as the number of coins' )

						# Main calculation
						def get_distrib(a__ ) -> CoinsDistribResult:

												if node is None:
																		return CoinsDistribResult(0     ,      1 )

												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			get_distrib(node.left )
												lowerCAmelCase	, lowerCAmelCase   :str						=			get_distrib(node.right )

												lowerCAmelCase   :str						=			1 - left_distrib_excess
												lowerCAmelCase   :Optional[Any]						=			1 - right_distrib_excess

												lowerCAmelCase   :Any						=			(
												    left_distrib_moves
												    + right_distrib_moves
												    + abs(a__ )
												    + abs(a__ )
												)
												lowerCAmelCase   :Tuple						=			node.data - coins_to_left - coins_to_right

												return CoinsDistribResult(a__     ,      a__ )

						return get_distrib(a__ )[0]


if __name__ == "__main__":
		import doctest

		doctest.testmod()